Immune multi-agent model using vaccine for cooperative air-defense system of systems for surface warship formation based on danger theory

Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune system （BIS） according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS, and then designs the models of components and the architecture for a monitoring agent, a regulating agent, a killer agent, a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system, the multi-agent system （MAS）, the vaccine and the danger theory （DT）. Moreover a new immune multi-agent model using vaccine based on DT （IMMUVBDT） for the cooperative air-defense SoS is advanced. The immune response and immune mechanism of the CASoSSWF are analyzed. The model has a capability of memory, evolution, commendable dynamic environment adaptability and self-learning, and embodies adequately the cooperative air-defense mechanism for the CASoSSWF. Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.

Saliency detection of infrared image based on region covariance and global feature

In order to better represent infrared target features under different environments, a saliency detection method based on region covariance and global feature is proposed. Firstly, the region covariance features on different scale spaces and different image regions are extracted and transformed into sigma features,then combined with central position feature, the local salient map is generated. Next, a global salient map is generated by gray contrast and density estimation. Finally, the saliency detection result of infrared images is obtained by fusing the local and global salient maps. The experimental results show that the salient map of the proposed method has complete target features and obvious edges,and the proposed method is better than the state of art method both qualitatively and quantitatively.

Visualization analysis of the capability of weapon system of systems for multi-dimensional indicators

In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly and intuitively by visualization of multi-dimensional indicators. A method of machine learning and visualization is proposed, which can display and analyze the capabilities of different WSOS in a two-dimensional plane. The analysis and comparison of the comprehensive capability of different components of WSOS is realized by the method, which consists of six parts: multiple simulations, key indicators mining, three spatial distance calculation, fusion project calculation, calculation of individual capability density, and calculation of multiple capability ranges overlay. Binding a simulation experiment, the collaborative analysis of six indicators and 100 possible kinds of red WSOS are achieved. The experimental results show that this method can effectively improve the quality and speed of capabilities analysis, reveal a large number of potential information, and provide a visual support for the qualitative and quantitative analysis model.

Direction of arrival estimation and signal recovery based on single snapshot compressed sensing in frequency domain

Direction of arrival （DOA） estimation and signal recovery is the base of the under- water target localization, tracking and recognition. Based on the compressed sensing theory, a method for DOA estimation and source signal recovery is proposed using the single snap- shot processing of the received array signal in frequency domain. The received array signal are transformed to frequency domain, and the single snapshot data in frequency domain are re- garded as the measured data of the compressed sensing. According to the frequency, searching orientation and array manifold, the overcomplete array manifold is constructed as the sensing matrix of the compressed sensing. Both the target signal and power of the searching orientation are estimated by the basis pursuit method to complete DOA estimation and signal recovery. Simulation results show that the proposed method has a number of advantages over the mini- mum variance distortionless response （MVDR） method, including improved robustness to noise, fewer requirement in number of sensors and snapshots. And the correlation coefficient of the signal reaches up to 0.89. Experiment results in real environments verify that the proposed method performs more effectively in the detection of weak targets than the MVDR method and can be applied to real sonar system.

An Improved Approximation Algorithm for the k-Level Facility Location Problem with Soft Capacities

We consider the k-level facility location problem with soft capacities （k-LFLPSC）. In the k- LFLPSC, each facility i has a soft capacity ui along with an initial opening cost fi ≥O, i.e., the capacity of facility i is an integer multiple of ui incurring a cost equals to the corresponding multiple of fi. We firstly propose a new bifactor （ln（1/β）/（1 -β）, 1 ＋ 2/（1 - β））-approximation algorithm for the k-level facility location problem （k-LFLP）, where β∈（0, 1） is a fixed constant. Then, we give a reduction from the k-LFLPSC to the k-LFLP. The reduction together with the above bifactor approximation algorithm for the k-LFLP imply a 5.5053-approximation algorithm for the k-LFLPSC which improves the previous 6-approximation.

Minimizing Ratio of Monotone Non-submodular Functions

In this paper,we investigate the problem of minimizing the ratio of normalized non-negative monotone non-submodular set function f and normalized non-negative monotone set function g.We take advantage of the greedy technique and get a per-formance guarantee depending on the generalized curvature and inverse generalized curvature of f,as well as the submodularity ratio of g.Our results generalize the works of Bai et al.(Algorithms for optimizing the ratio of submodular functions.In:Proceedings of the 33rd International Conference on Machine L earning,2016)and Qian et al.(Optimizing ratio of monotone set functions.In:Proceedings of the 26th International Joint Conference on Artificial Intelligence,2017).

A Primal-Dual Algorithm for the Generalized Prize-Collecting Steiner Forest Problem

In this paper,we consider the generalized prize-collecting Steiner forest problem,extending the prize-collecting Steiner forest problem.In this problem,we are given a connected graph G=(V,E)and a set of vertex sets V={V1,V2,…,Vl}.Every edge in E has a nonnegative cost,and every vertex set in V has a nonnegative penalty cost.For a given edge set F⊆E,vertex set Vi∈V is said to be connected by edge set F if Vi is in a connected component of the F-spanned subgraph.The objective is to find such an edge set F such that the total edge cost in F and the penalty cost of the vertex sets not connected by F is minimized.Our main contribution is to give a 3-approximation algorithm for this problem via the primal-dual method.

Degree dependence entropy descriptor for complex networks

In order to supply better accordance for mod eling and simulation of complex networks, a new degree dependence entropy （DDE） descriptor is proposed to describe the degree dependence relationship and corre sponding characteristic in this paper. First of all, degrees of vertices and the shortest path lengths between all pairs of ,ertices are computed. Then the degree dependence matrices under different shortest path lengths are con structed. At last the DDEs are extracted from the degree dependence matrices. Simulation results show that the DDE descriptor can reflect the complexity of degree dependence relationship in complex networks; high DDE indicates complex degree dependence relationship; low DDE indicates the opposite one. The DDE can be seen as a quantitative statistical characteristic, which is meaningful for networked modeling and simulation.